Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations46826
Missing cells1312
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.0 MiB
Average record size in memory672.9 B

Variable types

Numeric7
Categorical17
Unsupported1
Text1

Alerts

driverRoom is highly overall correlated with locationHigh correlation
location is highly overall correlated with driverRoomHigh correlation
price is highly overall correlated with space and 1 other fieldsHigh correlation
space is highly overall correlated with priceHigh correlation
square price is highly overall correlated with priceHigh correlation
kitchen is highly imbalanced (85.4%) Imbalance
garage is highly imbalanced (55.6%) Imbalance
furnihsed is highly imbalanced (60.2%) Imbalance
basement is highly imbalanced (76.8%) Imbalance
lounges has 1118 (2.4%) missing values Missing
Unnamed: 0 has unique values Unique
apartments is an unsupported type, check if it needs cleaning or further analysis Unsupported
propertyAge has 37348 (79.8%) zeros Zeros

Reproduction

Analysis started2025-02-16 15:41:32.454524
Analysis finished2025-02-16 15:41:50.409279
Duration17.95 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

Unique 

Distinct46826
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25915.132
Minimum0
Maximum51844
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size366.0 KiB
2025-02-16T15:41:50.582429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2597.25
Q112925.25
median25836.5
Q338917.75
95-th percentile49286.75
Maximum51844
Range51844
Interquartile range (IQR)25992.5

Descriptive statistics

Standard deviation14993.821
Coefficient of variation (CV)0.57857398
Kurtosis-1.2053547
Mean25915.132
Median Absolute Deviation (MAD)12996.5
Skewness0.003156046
Sum1.213502 × 109
Variance2.2481468 × 108
MonotonicityStrictly increasing
2025-02-16T15:41:50.912826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
34654 1
 
< 0.1%
34656 1
 
< 0.1%
34657 1
 
< 0.1%
34658 1
 
< 0.1%
34659 1
 
< 0.1%
34660 1
 
< 0.1%
34662 1
 
< 0.1%
34663 1
 
< 0.1%
34664 1
 
< 0.1%
Other values (46816) 46816
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
51844 1
< 0.1%
51843 1
< 0.1%
51842 1
< 0.1%
51841 1
< 0.1%
51840 1
< 0.1%
51839 1
< 0.1%
51838 1
< 0.1%
51837 1
< 0.1%
51836 1
< 0.1%
51835 1
< 0.1%

front
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
شمالية
12160 
شرقية
10613 
جنوبية
10216 
غربية
6985 
شمالية شرقية
1982 
Other values (5)
4870 

Length

Max length12
Median length10
Mean length6.4868236
Min length5

Characters and Unicode

Total characters303752
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowشرقية
2nd rowغربية
3rd rowجنوبية شرقية
4th rowغربية
5th rowشمالية

Common Values

ValueCountFrequency (%)
شمالية 12160
26.0%
شرقية 10613
22.7%
جنوبية 10216
21.8%
غربية 6985
14.9%
شمالية شرقية 1982
 
4.2%
جنوبية غربية 1599
 
3.4%
جنوبية شرقية 1503
 
3.2%
شمالية غربية 1409
 
3.0%
ثلاث شوارع 315
 
0.7%
أربع شوارع 44
 
0.1%

Length

2025-02-16T15:41:51.097621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-16T15:41:51.241189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
شمالية 15551
29.0%
شرقية 14098
26.3%
جنوبية 13318
24.8%
غربية 9993
18.6%
شوارع 359
 
0.7%
ثلاث 315
 
0.6%
أربع 44
 
0.1%

Most occurring characters

ValueCountFrequency (%)
ي 52960
17.4%
ة 52960
17.4%
ش 30008
9.9%
ر 24494
8.1%
ب 23355
7.7%
ا 16225
 
5.3%
ل 15866
 
5.2%
م 15551
 
5.1%
ق 14098
 
4.6%
و 13677
 
4.5%
Other values (7) 44558
14.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 303752
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
ي 52960
17.4%
ة 52960
17.4%
ش 30008
9.9%
ر 24494
8.1%
ب 23355
7.7%
ا 16225
 
5.3%
ل 15866
 
5.2%
م 15551
 
5.1%
ق 14098
 
4.6%
و 13677
 
4.5%
Other values (7) 44558
14.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 303752
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
ي 52960
17.4%
ة 52960
17.4%
ش 30008
9.9%
ر 24494
8.1%
ب 23355
7.7%
ا 16225
 
5.3%
ل 15866
 
5.2%
م 15551
 
5.1%
ق 14098
 
4.6%
و 13677
 
4.5%
Other values (7) 44558
14.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 303752
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
ي 52960
17.4%
ة 52960
17.4%
ش 30008
9.9%
ر 24494
8.1%
ب 23355
7.7%
ا 16225
 
5.3%
ل 15866
 
5.2%
م 15551
 
5.1%
ق 14098
 
4.6%
و 13677
 
4.5%
Other values (7) 44558
14.7%

rooms
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6874386
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 KiB
2025-02-16T15:41:51.412793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median5
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1049696
Coefficient of variation (CV)0.23572994
Kurtosis1.3400869
Mean4.6874386
Median Absolute Deviation (MAD)1
Skewness0.029706091
Sum219494
Variance1.2209579
MonotonicityNot monotonic
2025-02-16T15:41:51.529719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 19874
42.4%
4 16274
34.8%
7 4472
 
9.6%
3 3027
 
6.5%
6 2243
 
4.8%
1 687
 
1.5%
2 249
 
0.5%
ValueCountFrequency (%)
1 687
 
1.5%
2 249
 
0.5%
3 3027
 
6.5%
4 16274
34.8%
5 19874
42.4%
6 2243
 
4.8%
7 4472
 
9.6%
ValueCountFrequency (%)
7 4472
 
9.6%
6 2243
 
4.8%
5 19874
42.4%
4 16274
34.8%
3 3027
 
6.5%
2 249
 
0.5%
1 687
 
1.5%

lounges
Categorical

Missing 

Distinct6
Distinct (%)< 0.1%
Missing1118
Missing (%)2.4%
Memory size2.6 MiB
2
19719 
3
12992 
1
7996 
4
2832 
5
2156 

Length

Max length2
Median length1
Mean length1.0002844
Min length1

Characters and Unicode

Total characters45721
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row2
4th row3
5th row2

Common Values

ValueCountFrequency (%)
2 19719
42.1%
3 12992
27.7%
1 7996
17.1%
4 2832
 
6.0%
5 2156
 
4.6%
7+ 13
 
< 0.1%
(Missing) 1118
 
2.4%

Length

2025-02-16T15:41:51.660408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-16T15:41:52.120505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 19719
43.1%
3 12992
28.4%
1 7996
17.5%
4 2832
 
6.2%
5 2156
 
4.7%
7 13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 19719
43.1%
3 12992
28.4%
1 7996
17.5%
4 2832
 
6.2%
5 2156
 
4.7%
7 13
 
< 0.1%
+ 13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45721
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 19719
43.1%
3 12992
28.4%
1 7996
17.5%
4 2832
 
6.2%
5 2156
 
4.7%
7 13
 
< 0.1%
+ 13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45721
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 19719
43.1%
3 12992
28.4%
1 7996
17.5%
4 2832
 
6.2%
5 2156
 
4.7%
7 13
 
< 0.1%
+ 13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45721
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 19719
43.1%
3 12992
28.4%
1 7996
17.5%
4 2832
 
6.2%
5 2156
 
4.7%
7 13
 
< 0.1%
+ 13
 
< 0.1%

bathrooms
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
5+
32371 
4
8781 
3
4123 
2
 
1227
1
 
324

Length

Max length2
Median length2
Mean length1.691304
Min length1

Characters and Unicode

Total characters79197
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5+
2nd row5+
3rd row5+
4th row5+
5th row4

Common Values

ValueCountFrequency (%)
5+ 32371
69.1%
4 8781
 
18.8%
3 4123
 
8.8%
2 1227
 
2.6%
1 324
 
0.7%

Length

2025-02-16T15:41:52.271795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-16T15:41:52.407346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5 32371
69.1%
4 8781
 
18.8%
3 4123
 
8.8%
2 1227
 
2.6%
1 324
 
0.7%

Most occurring characters

ValueCountFrequency (%)
5 32371
40.9%
+ 32371
40.9%
4 8781
 
11.1%
3 4123
 
5.2%
2 1227
 
1.5%
1 324
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 79197
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 32371
40.9%
+ 32371
40.9%
4 8781
 
11.1%
3 4123
 
5.2%
2 1227
 
1.5%
1 324
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 79197
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 32371
40.9%
+ 32371
40.9%
4 8781
 
11.1%
3 4123
 
5.2%
2 1227
 
1.5%
1 324
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 79197
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 32371
40.9%
+ 32371
40.9%
4 8781
 
11.1%
3 4123
 
5.2%
2 1227
 
1.5%
1 324
 
0.4%

streetWidth
Real number (ℝ)

Distinct55
Distinct (%)0.1%
Missing190
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean18.158418
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 KiB
2025-02-16T15:41:52.574909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q115
median20
Q320
95-th percentile25
Maximum100
Range99
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.1678661
Coefficient of variation (CV)0.22952803
Kurtosis38.026548
Mean18.158418
Median Absolute Deviation (MAD)2
Skewness2.5035774
Sum846836
Variance17.371108
MonotonicityNot monotonic
2025-02-16T15:41:52.774943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 19160
40.9%
15 15392
32.9%
18 2956
 
6.3%
25 1948
 
4.2%
16 1122
 
2.4%
21 908
 
1.9%
12 680
 
1.5%
30 635
 
1.4%
10 589
 
1.3%
17 588
 
1.3%
Other values (45) 2658
 
5.7%
ValueCountFrequency (%)
1 21
 
< 0.1%
2 3
 
< 0.1%
4 5
 
< 0.1%
5 491
1.0%
6 4
 
< 0.1%
8 8
 
< 0.1%
9 9
 
< 0.1%
10 589
1.3%
11 32
 
0.1%
12 680
1.5%
ValueCountFrequency (%)
100 7
< 0.1%
97 1
 
< 0.1%
96 2
 
< 0.1%
72 2
 
< 0.1%
70 1
 
< 0.1%
64 1
 
< 0.1%
62 1
 
< 0.1%
61 1
 
< 0.1%
60 10
< 0.1%
56 2
 
< 0.1%

stairs
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
1
32028 
0
14798 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46826
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 32028
68.4%
0 14798
31.6%

Length

2025-02-16T15:41:52.930824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-16T15:41:53.020218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 32028
68.4%
0 14798
31.6%

Most occurring characters

ValueCountFrequency (%)
1 32028
68.4%
0 14798
31.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 32028
68.4%
0 14798
31.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 32028
68.4%
0 14798
31.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 32028
68.4%
0 14798
31.6%

propertyAge
Real number (ℝ)

Zeros 

Distinct36
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4547901
Minimum0
Maximum35
Zeros37348
Zeros (%)79.8%
Negative0
Negative (%)0.0%
Memory size366.0 KiB
2025-02-16T15:41:53.134603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile17
Maximum35
Range35
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.4968328
Coefficient of variation (CV)2.6465941
Kurtosis10.288843
Mean2.4547901
Median Absolute Deviation (MAD)0
Skewness3.2063296
Sum114948
Variance42.208837
MonotonicityNot monotonic
2025-02-16T15:41:53.305892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 37348
79.8%
5 708
 
1.5%
6 608
 
1.3%
4 591
 
1.3%
10 578
 
1.2%
7 566
 
1.2%
8 540
 
1.2%
1 527
 
1.1%
3 496
 
1.1%
2 488
 
1.0%
Other values (26) 4376
 
9.3%
ValueCountFrequency (%)
0 37348
79.8%
1 527
 
1.1%
2 488
 
1.0%
3 496
 
1.1%
4 591
 
1.3%
5 708
 
1.5%
6 608
 
1.3%
7 566
 
1.2%
8 540
 
1.2%
9 397
 
0.8%
ValueCountFrequency (%)
35 365
0.8%
34 45
 
0.1%
33 67
 
0.1%
32 53
 
0.1%
31 34
 
0.1%
30 427
0.9%
29 42
 
0.1%
28 69
 
0.1%
27 75
 
0.2%
26 47
 
0.1%

driverRoom
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
0
30162 
1
16664 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46826
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 30162
64.4%
1 16664
35.6%

Length

2025-02-16T15:41:53.487102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-16T15:41:53.570154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 30162
64.4%
1 16664
35.6%

Most occurring characters

ValueCountFrequency (%)
0 30162
64.4%
1 16664
35.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 30162
64.4%
1 16664
35.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 30162
64.4%
1 16664
35.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 30162
64.4%
1 16664
35.6%

tent
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
0
24993 
1
21833 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46826
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 24993
53.4%
1 21833
46.6%

Length

2025-02-16T15:41:53.684023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-16T15:41:53.776969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 24993
53.4%
1 21833
46.6%

Most occurring characters

ValueCountFrequency (%)
0 24993
53.4%
1 21833
46.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 24993
53.4%
1 21833
46.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 24993
53.4%
1 21833
46.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 24993
53.4%
1 21833
46.6%

patio
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
1
36027 
0
10799 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46826
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 36027
76.9%
0 10799
 
23.1%

Length

2025-02-16T15:41:53.882578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-16T15:41:53.979343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 36027
76.9%
0 10799
 
23.1%

Most occurring characters

ValueCountFrequency (%)
1 36027
76.9%
0 10799
 
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 36027
76.9%
0 10799
 
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 36027
76.9%
0 10799
 
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 36027
76.9%
0 10799
 
23.1%

kitchen
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
1
45848 
0
 
978

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46826
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 45848
97.9%
0 978
 
2.1%

Length

2025-02-16T15:41:54.105989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-16T15:41:54.186379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 45848
97.9%
0 978
 
2.1%

Most occurring characters

ValueCountFrequency (%)
1 45848
97.9%
0 978
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 45848
97.9%
0 978
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 45848
97.9%
0 978
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 45848
97.9%
0 978
 
2.1%

outdoorRoom
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
1
30208 
0
16618 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46826
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 30208
64.5%
0 16618
35.5%

Length

2025-02-16T15:41:54.285000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-16T15:41:54.394512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 30208
64.5%
0 16618
35.5%

Most occurring characters

ValueCountFrequency (%)
1 30208
64.5%
0 16618
35.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 30208
64.5%
0 16618
35.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 30208
64.5%
0 16618
35.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 30208
64.5%
0 16618
35.5%

garage
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
1
42512 
0
4314 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46826
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 42512
90.8%
0 4314
 
9.2%

Length

2025-02-16T15:41:54.500147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-16T15:41:54.590852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 42512
90.8%
0 4314
 
9.2%

Most occurring characters

ValueCountFrequency (%)
1 42512
90.8%
0 4314
 
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 42512
90.8%
0 4314
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 42512
90.8%
0 4314
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 42512
90.8%
0 4314
 
9.2%

duplex
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
0
32886 
1
13940 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46826
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 32886
70.2%
1 13940
29.8%

Length

2025-02-16T15:41:54.693231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-16T15:41:54.775644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 32886
70.2%
1 13940
29.8%

Most occurring characters

ValueCountFrequency (%)
0 32886
70.2%
1 13940
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 32886
70.2%
1 13940
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 32886
70.2%
1 13940
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 32886
70.2%
1 13940
29.8%

space
Real number (ℝ)

High correlation 

Distinct742
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean380.02174
Minimum50
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 KiB
2025-02-16T15:41:54.913300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile200
Q1270
median312
Q3400
95-th percentile750
Maximum10000
Range9950
Interquartile range (IQR)130

Descriptive statistics

Standard deviation281.43127
Coefficient of variation (CV)0.74056624
Kurtosis239.9829
Mean380.02174
Median Absolute Deviation (MAD)62
Skewness11.396691
Sum17794898
Variance79203.561
MonotonicityNot monotonic
2025-02-16T15:41:55.109993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300 7782
 
16.6%
250 3176
 
6.8%
200 2934
 
6.3%
375 2338
 
5.0%
360 2256
 
4.8%
312 1632
 
3.5%
450 1371
 
2.9%
400 1238
 
2.6%
350 845
 
1.8%
500 832
 
1.8%
Other values (732) 22422
47.9%
ValueCountFrequency (%)
50 6
< 0.1%
84 1
 
< 0.1%
100 1
 
< 0.1%
111 1
 
< 0.1%
118 2
 
< 0.1%
124 1
 
< 0.1%
134 1
 
< 0.1%
135 1
 
< 0.1%
138 3
< 0.1%
140 2
 
< 0.1%
ValueCountFrequency (%)
10000 2
 
< 0.1%
9178 2
 
< 0.1%
9080 1
 
< 0.1%
8581 3
< 0.1%
6100 2
 
< 0.1%
6000 1
 
< 0.1%
5933 1
 
< 0.1%
5200 2
 
< 0.1%
5100 5
< 0.1%
5040 1
 
< 0.1%

apartments
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size2.3 MiB

maidRoom
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
1
35954 
0
10872 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46826
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 35954
76.8%
0 10872
 
23.2%

Length

2025-02-16T15:41:55.267036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-16T15:41:55.353252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 35954
76.8%
0 10872
 
23.2%

Most occurring characters

ValueCountFrequency (%)
1 35954
76.8%
0 10872
 
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 35954
76.8%
0 10872
 
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 35954
76.8%
0 10872
 
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 35954
76.8%
0 10872
 
23.2%

elevator
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
0
35787 
1
11039 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46826
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 35787
76.4%
1 11039
 
23.6%

Length

2025-02-16T15:41:55.476792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-16T15:41:55.569192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 35787
76.4%
1 11039
 
23.6%

Most occurring characters

ValueCountFrequency (%)
0 35787
76.4%
1 11039
 
23.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 35787
76.4%
1 11039
 
23.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 35787
76.4%
1 11039
 
23.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 35787
76.4%
1 11039
 
23.6%

furnihsed
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
0
43139 
1
 
3687

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46826
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 43139
92.1%
1 3687
 
7.9%

Length

2025-02-16T15:41:55.673572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-16T15:41:55.753623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 43139
92.1%
1 3687
 
7.9%

Most occurring characters

ValueCountFrequency (%)
0 43139
92.1%
1 3687
 
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 43139
92.1%
1 3687
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 43139
92.1%
1 3687
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 43139
92.1%
1 3687
 
7.9%

pool
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
0
41375 
1
5451 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46826
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 41375
88.4%
1 5451
 
11.6%

Length

2025-02-16T15:41:55.864760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-16T15:41:55.975883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 41375
88.4%
1 5451
 
11.6%

Most occurring characters

ValueCountFrequency (%)
0 41375
88.4%
1 5451
 
11.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 41375
88.4%
1 5451
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 41375
88.4%
1 5451
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 41375
88.4%
1 5451
 
11.6%

basement
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
0
45062 
1
 
1764

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46826
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 45062
96.2%
1 1764
 
3.8%

Length

2025-02-16T15:41:56.085513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-16T15:41:56.165824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 45062
96.2%
1 1764
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 45062
96.2%
1 1764
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 45062
96.2%
1 1764
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 45062
96.2%
1 1764
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 45062
96.2%
1 1764
 
3.8%
Distinct295
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
2025-02-16T15:41:56.586776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length24
Mean length7.9919276
Min length2

Characters and Unicode

Total characters374230
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)0.1%

Sample

1st row عكاظ
2nd row المهدية
3rd row الشفا
4th row ظهرة لبن
5th row قرطبة
ValueCountFrequency (%)
طويق 7775
 
15.1%
الرمال 4421
 
8.6%
عكاظ 3235
 
6.3%
النرجس 3188
 
6.2%
بدر 2757
 
5.3%
العارض 2324
 
4.5%
المونسية 1914
 
3.7%
الملقا 1651
 
3.2%
المهدية 1346
 
2.6%
ظهرة 1278
 
2.5%
Other values (292) 21762
42.1%
2025-02-16T15:41:57.183908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
97371
26.0%
ا 51157
13.7%
ل 40873
10.9%
ي 25125
 
6.7%
ر 20111
 
5.4%
م 15380
 
4.1%
و 12199
 
3.3%
ق 11625
 
3.1%
ن 11465
 
3.1%
ة 11428
 
3.1%
Other values (28) 77496
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 374230
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
97371
26.0%
ا 51157
13.7%
ل 40873
10.9%
ي 25125
 
6.7%
ر 20111
 
5.4%
م 15380
 
4.1%
و 12199
 
3.3%
ق 11625
 
3.1%
ن 11465
 
3.1%
ة 11428
 
3.1%
Other values (28) 77496
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 374230
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
97371
26.0%
ا 51157
13.7%
ل 40873
10.9%
ي 25125
 
6.7%
ر 20111
 
5.4%
م 15380
 
4.1%
و 12199
 
3.3%
ق 11625
 
3.1%
ن 11465
 
3.1%
ة 11428
 
3.1%
Other values (28) 77496
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 374230
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
97371
26.0%
ا 51157
13.7%
ل 40873
10.9%
ي 25125
 
6.7%
ر 20111
 
5.4%
م 15380
 
4.1%
و 12199
 
3.3%
ق 11625
 
3.1%
ن 11465
 
3.1%
ة 11428
 
3.1%
Other values (28) 77496
20.7%

location
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
غرب الرياض
13548 
شرق الرياض
12844 
شمال الرياض
11254 
جنوب الرياض
8960 
وسط الرياض
 
220

Length

Max length11
Median length10
Mean length10.431683
Min length10

Characters and Unicode

Total characters488474
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowجنوب الرياض
2nd rowغرب الرياض
3rd rowجنوب الرياض
4th rowغرب الرياض
5th rowشرق الرياض

Common Values

ValueCountFrequency (%)
غرب الرياض 13548
28.9%
شرق الرياض 12844
27.4%
شمال الرياض 11254
24.0%
جنوب الرياض 8960
19.1%
وسط الرياض 220
 
0.5%

Length

2025-02-16T15:41:57.307806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-16T15:41:57.417857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
الرياض 46826
50.0%
غرب 13548
 
14.5%
شرق 12844
 
13.7%
شمال 11254
 
12.0%
جنوب 8960
 
9.6%
وسط 220
 
0.2%

Most occurring characters

ValueCountFrequency (%)
ا 104906
21.5%
ر 73218
15.0%
ل 58080
11.9%
46826
9.6%
ي 46826
9.6%
ض 46826
9.6%
ش 24098
 
4.9%
ب 22508
 
4.6%
غ 13548
 
2.8%
ق 12844
 
2.6%
Other values (6) 38794
 
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 488474
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
ا 104906
21.5%
ر 73218
15.0%
ل 58080
11.9%
46826
9.6%
ي 46826
9.6%
ض 46826
9.6%
ش 24098
 
4.9%
ب 22508
 
4.6%
غ 13548
 
2.8%
ق 12844
 
2.6%
Other values (6) 38794
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 488474
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
ا 104906
21.5%
ر 73218
15.0%
ل 58080
11.9%
46826
9.6%
ي 46826
9.6%
ض 46826
9.6%
ش 24098
 
4.9%
ب 22508
 
4.6%
غ 13548
 
2.8%
ق 12844
 
2.6%
Other values (6) 38794
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 488474
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
ا 104906
21.5%
ر 73218
15.0%
ل 58080
11.9%
46826
9.6%
ي 46826
9.6%
ض 46826
9.6%
ش 24098
 
4.9%
ب 22508
 
4.6%
غ 13548
 
2.8%
ق 12844
 
2.6%
Other values (6) 38794
 
7.9%

price
Real number (ℝ)

High correlation 

Distinct672
Distinct (%)1.4%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2347636.4
Minimum1080
Maximum1 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 KiB
2025-02-16T15:41:57.612973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1080
5-th percentile894150
Q11180000
median1600000
Q32600000
95-th percentile5650000
Maximum1 × 108
Range99998920
Interquartile range (IQR)1420000

Descriptive statistics

Standard deviation2782052.8
Coefficient of variation (CV)1.1850441
Kurtosis158.1017
Mean2347636.4
Median Absolute Deviation (MAD)550000
Skewness9.4820898
Sum1.0992573 × 1011
Variance7.7398176 × 1012
MonotonicityNot monotonic
2025-02-16T15:41:57.808594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200000 1591
 
3.4%
1100000 1440
 
3.1%
1600000 1333
 
2.8%
1300000 1270
 
2.7%
1500000 1253
 
2.7%
1250000 1225
 
2.6%
1400000 1162
 
2.5%
1150000 1157
 
2.5%
2200000 1136
 
2.4%
3000000 1126
 
2.4%
Other values (662) 34131
72.9%
ValueCountFrequency (%)
1080 3
 
< 0.1%
50000 3
 
< 0.1%
70000 1
 
< 0.1%
90000 1
 
< 0.1%
95000 1
 
< 0.1%
100000 13
< 0.1%
101100 1
 
< 0.1%
108000 1
 
< 0.1%
111111 1
 
< 0.1%
113000 1
 
< 0.1%
ValueCountFrequency (%)
100000000 1
 
< 0.1%
90000000 1
 
< 0.1%
60000000 3
 
< 0.1%
56500000 2
 
< 0.1%
55000000 13
< 0.1%
50000000 4
 
< 0.1%
45000000 11
< 0.1%
42000000 1
 
< 0.1%
40000000 4
 
< 0.1%
39000000 2
 
< 0.1%

square price
Real number (ℝ)

High correlation 

Distinct4917
Distinct (%)10.5%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean6088.0588
Minimum3.4285714
Maximum136144.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 KiB
2025-02-16T15:41:58.006598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.4285714
5-th percentile2722.2222
Q13866.6667
median5000
Q37600
95-th percentile11750
Maximum136144.58
Range136141.15
Interquartile range (IQR)3733.3333

Descriptive statistics

Standard deviation4515.8452
Coefficient of variation (CV)0.74175454
Kurtosis181.62589
Mean6088.0588
Median Absolute Deviation (MAD)1410.2564
Skewness10.214996
Sum2.8506727 × 108
Variance20392858
MonotonicityNot monotonic
2025-02-16T15:41:58.203795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 1198
 
2.6%
4000 997
 
2.1%
4166.666667 558
 
1.2%
5500 532
 
1.1%
3333.333333 485
 
1.0%
5333.333333 463
 
1.0%
10000 439
 
0.9%
4500 432
 
0.9%
8000 427
 
0.9%
4400 407
 
0.9%
Other values (4907) 40886
87.3%
ValueCountFrequency (%)
3.428571429 3
< 0.1%
100 1
 
< 0.1%
116.2068966 1
 
< 0.1%
124.2236025 1
 
< 0.1%
125 3
< 0.1%
200 1
 
< 0.1%
206.6666667 1
 
< 0.1%
222.222 1
 
< 0.1%
246.3054187 1
 
< 0.1%
256.4102564 3
< 0.1%
ValueCountFrequency (%)
136144.5783 2
 
< 0.1%
116666.6667 2
 
< 0.1%
116279.0698 1
 
< 0.1%
110000 1
 
< 0.1%
106481.4815 5
< 0.1%
101333.3333 1
 
< 0.1%
100000 1
 
< 0.1%
97222.56944 1
 
< 0.1%
94341.8239 1
 
< 0.1%
92000 1
 
< 0.1%

Interactions

2025-02-16T15:41:47.244375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:39.113554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:40.563703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:41.681604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:42.815738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:45.004433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:46.119239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:47.432818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:39.262574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:40.713981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:41.829331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:42.999051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:45.162701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:46.268949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:47.627612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:39.494364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:40.892174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:42.003107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:44.181041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:45.310843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:46.443652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:47.787319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:39.907939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:41.048444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:42.145455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:44.331226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:45.485988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:46.604721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:47.990654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:40.073285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:41.201022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:42.305204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:44.493192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:45.641835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:46.764805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:48.273292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:40.216425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:41.355644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:42.486076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:44.649225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:45.788561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:46.917629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:48.588178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:40.383963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:41.513438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:42.648889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:44.812076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:45.939411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T15:41:47.081835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-16T15:41:58.389811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Unnamed: 0basementbathroomsdriverRoomduplexelevatorfrontfurnihsedgaragekitchenlocationloungesmaidRoomoutdoorRoompatiopoolpricepropertyAgeroomsspacesquare pricestairsstreetWidthtent
Unnamed: 01.0000.0360.0310.0900.0910.0240.0160.0410.0620.0320.0640.0250.0450.0430.0480.0380.0320.0140.0110.0140.0260.055-0.0110.053
basement0.0361.0000.0340.1080.0260.0680.1530.1990.0290.0210.0960.0930.0650.0550.0520.1700.1670.0840.0360.2260.0800.0150.0480.027
bathrooms0.0310.0341.0000.1090.0850.0350.0270.0310.2540.0380.0930.1530.2080.1470.2520.0420.0120.0560.2430.0300.0330.1510.0490.206
driverRoom0.0900.1080.1091.0000.2640.3630.1390.0920.1070.0430.6180.1570.3130.1460.0240.3170.1150.2730.0720.1520.1440.1030.0100.277
duplex0.0910.0260.0850.2641.0000.1620.1110.0410.0600.0470.3820.1460.0400.0200.0660.1010.0560.2060.1350.0730.0660.0900.0550.165
elevator0.0240.0680.0350.3630.1621.0000.0600.0110.0890.0340.4990.1140.2340.0880.0180.3340.1170.1800.0830.0690.1780.1940.0540.231
front0.0160.1530.0270.1390.1110.0601.0000.0890.0110.0080.0650.0510.0300.0350.0300.0900.1500.0710.0550.1690.0490.0330.0440.070
furnihsed0.0410.1990.0310.0920.0410.0110.0891.0000.0570.0380.0580.0650.0640.0730.0970.0990.0760.1430.0400.1370.0320.0460.0310.162
garage0.0620.0290.2540.1070.0600.0890.0110.0571.0000.0980.1080.0960.3460.3020.3890.0600.0170.1090.1620.0130.0200.1300.1230.219
kitchen0.0320.0210.0380.0430.0470.0340.0080.0380.0981.0000.0720.0850.1050.0710.0700.0350.0000.0660.0530.0000.0440.0300.0380.069
location0.0640.0960.0930.6180.3820.4990.0650.0580.1080.0721.0000.0860.2390.0620.1490.3840.0710.1570.0810.0610.1030.1620.0680.444
lounges0.0250.0930.1530.1570.1460.1140.0510.0650.0960.0850.0861.0000.2130.1030.1040.1250.0540.0570.1000.0690.0330.1350.0310.099
maidRoom0.0450.0650.2080.3130.0400.2340.0300.0640.3460.1050.2390.2131.0000.2690.1860.1520.0420.0780.1690.0490.0570.2320.0900.054
outdoorRoom0.0430.0550.1470.1460.0200.0880.0350.0730.3020.0710.0620.1030.2691.0000.3130.1010.0480.0390.1130.0600.0340.1170.0640.140
patio0.0480.0520.2520.0240.0660.0180.0300.0970.3890.0700.1490.1040.1860.3131.0000.0520.0170.0410.1900.0280.0380.0870.0760.330
pool0.0380.1700.0420.3170.1010.3340.0900.0990.0600.0350.3840.1250.1520.1010.0521.0000.1950.0380.0630.2050.2510.0860.0650.099
price0.0320.1670.0120.1150.0560.1170.1500.0760.0170.0000.0710.0540.0420.0480.0170.1951.0000.2360.0000.5440.7800.012-0.1370.029
propertyAge0.0140.0840.0560.2730.2060.1800.0710.1430.1090.0660.1570.0570.0780.0390.0410.0380.2361.0000.0470.423-0.0640.181-0.1260.099
rooms0.0110.0360.2430.0720.1350.0830.0550.0400.1620.0530.0810.1000.1690.1130.1900.0630.0000.0471.0000.091-0.0670.1690.0350.167
space0.0140.2260.0300.1520.0730.0690.1690.1370.0130.0000.0610.0690.0490.0600.0280.2050.5440.4230.0911.000-0.0210.013-0.0210.006
square price0.0260.0800.0330.1440.0660.1780.0490.0320.0200.0440.1030.0330.0570.0340.0380.2510.780-0.064-0.067-0.0211.0000.023-0.1300.066
stairs0.0550.0150.1510.1030.0900.1940.0330.0460.1300.0300.1620.1350.2320.1170.0870.0860.0120.1810.1690.0130.0231.0000.0750.004
streetWidth-0.0110.0480.0490.0100.0550.0540.0440.0310.1230.0380.0680.0310.0900.0640.0760.065-0.137-0.1260.035-0.021-0.1300.0751.0000.073
tent0.0530.0270.2060.2770.1650.2310.0700.1620.2190.0690.4440.0990.0540.1400.3300.0990.0290.0990.1670.0060.0660.0040.0731.000

Missing values

2025-02-16T15:41:49.090781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-16T15:41:49.571380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-16T15:41:50.131691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0frontroomsloungesbathroomsstreetWidthstairspropertyAgedriverRoomtentpatiokitchenoutdoorRoomgarageduplexspaceapartmentsmaidRoomelevatorfurnihsedpoolbasementneighbourhoodlocationpricesquare price
00شرقية545+20.0101111111300000000عكاظجنوب الرياض1050000.03500.000000
11غربية435+20.0101111110540211000المهديةغرب الرياض3000000.05555.555556
22جنوبية شرقية725+15.01311011110875010000الشفاجنوب الرياض2000000.02285.714286
33غربية735+15.0130111011200010100ظهرة لبنغرب الرياض894000.04470.000000
44شمالية42425.0101111110400311100قرطبةشرق الرياض3500000.08750.000000
55شمالية شرقية725+10.0100111010500000000السويديغرب الرياض800000.01600.000000
66شمالية625+20.0100111111275001000المهديةغرب الرياض2100000.07636.363636
77شمالية735+25.0160111111300010000عكاظجنوب الرياض1100000.03666.666667
88جنوبية52414.00350011010585000000الروضةشرق الرياض1900000.03247.863248
99شمالية شرقية555+22.0100011110360010000عكاظجنوب الرياض1200000.03333.333333
Unnamed: 0frontroomsloungesbathroomsstreetWidthstairspropertyAgedriverRoomtentpatiokitchenoutdoorRoomgarageduplexspaceapartmentsmaidRoomelevatorfurnihsedpoolbasementneighbourhoodlocationpricesquare price
4681651835شرقية515+20.0101111110360210100طويقغرب الرياض1500000.04166.666667
4681751836شرقية52420.0081001000362000000المونسيةشرق الرياض2050000.05662.983425
4681851837شمالية535+15.0031100100372310000الرمالشرق الرياض1420000.03817.204301
4681951838غربية425+20.0170111110360200010طويقغرب الرياض1350000.03750.000000
4682051839جنوبية12315.0101101110360010100النهضةشرق الرياض2200000.06111.111111
4682151840جنوبية525+20.0100111110385111000المونسيةشرق الرياض2250000.05844.155844
4682251841غربية725+12.0100111110500010000ظهرة البديعةغرب الرياض1050000.02100.000000
4682351842غربية535+20.0000111111200010000طويقغرب الرياض1000000.05000.000000
4682451843جنوبية غربية1NaN115.0000001000405200000النهضةشرق الرياض2300000.05679.012346
4682551844شمالية625+15.0100111110750210000المونسيةشرق الرياض4100000.05466.666667